Method for determining a close approximate benefit of reducing memory footprint of a Java application

ABSTRACT

Changes in performance in a Java program are deduced from information related to garbage collection events of the program. Assumptions are made about the system, the application and garbage collection, and changes in performance that will result from modifying the program are deduced.

This application is a continuation of application Ser. No. 10/761,991,filed Jan. 21, 2004, status allowed.

BACKGROUND OF THE INVENTION

1. Technical Field

The present invention relates generally to program performancemanagement, and particularly to estimating the effect on performance ofa Java program due to modification of the program.

2. Description of Related Art

In recent years, the use of the Java platform, a product available fromSun Microsystems, Inc., has greatly increased. Particularly, with therise of the Internet, the Java programming language became a popularlanguage used by programmers for various types of applications, such asWeb applications, enterprise applications, etc. The reason behind thispopularity is the characteristics that Java programming languageprovides, for example, platform-independence and multi-threadingsupport.

Platform-independence is enabled in Java through the use of a JavaVirtual Machine (JVM). The JVM first translates a program written in theJava programming language into standard bytecodes, as defined in the JVMspecifications. When the program runs, the JVM interprets the bytecodesand executes each bytecode. Another technology that was introduced inJava is “Just-in-Time” (JIT) compilation. In this case, the bytecodesare compiled into native code before they are executed. The native codeis comprised of machine instruction that are specific to the platform inwhich the JVM is running. Thus, any computer platform may run Javaapplications as long as it contains a Java runtime environment, whichinterprets the bytecode and optionally compiles them into native code atruntime (hence the name) for a specific operating system.

The JVM specification uses garbage collection for memory management. TheJava programming language enables programmers to create objects withouthaving to destroy them explicitly when they are no longer needed. Thelanguage ensures that memory allotted to unused objects will bereclaimed eventually and put back to the heap. An object is unused ifthere are no other objects that reference it. The heap is a portion ofthe JVM runtime environment from where memory needed to create an objectis taken. Thus, the JVM maintains the free memory in the heap so itknows where to get them when needed. The JVM has a default size for aheap although users can specify the minimum and maximum size wheninvoking the JVM runtime. The heap starts at a minimum size and itcontinues to grow when more and more memory is needed by the program.However, it cannot exceed the maximum. The heap can also shrink in sizewhen it is determined that it is too big and there are not a lot ofobjects being created, and when it has been specified that heapshrinking is allowed to happen.

Garbage collection (GC) is an event that takes place when an objectneeds to be created but there is not enough free memory to create thatobject. A garbage collector thread executes to detect all unused objectsand return them to the heap as free memory. The goal is to be able tocollect enough free memory for the object that is being created. Garbagecollection events suspend all running threads (except the garbagecollection thread). Garbage collection events can be a performancebottleneck when they happen very frequently since during this time, noother threads can run and therefore no actual work can be done, thus,reducing throughput and increasing response time.

There are two major concerns with garbage collection: (1) the durationof a GC event—the longer a GC event takes place, the longer the otherthreads are suspended. Thus, a very long GC event can be noticeablethrough poor response time. The duration of a GC event depends on thefootprint and the size of the heap. The footprint is the amount of usedmemory (or active objects); (2) the frequency of GC events—the moreoften a GC event takes place, the more time threads are being suspendedand therefore throughput is reduced. The frequency of GC events dependsalso on the footprint, size of the heap and the allocation rate, thatis, how fast is the program creating objects.

Programmers often attempt to reduce this burden on the system byminimizing GC events which can be achieved by creating objectsconservatively. Having said this, the use of objects is a key factor foroptimizing performance of Java programs. The use of objects affects thefootprint required for an application. The smaller the footprint, thebetter the performance. Thus, the questions often asked are: Can wereduce the footprint of a Java application? How can we reduce it? Do weknow how much improvement we will get if we reduce the footprint? Theanswer to the first question is easy to determine as programmers caninvestigate in their programs if they can still optimize the use ofobjects. For the second question, there are known ways to reducefootprint—object pools, object reuse, etc. For the last question, theactual benefit manifests itself in terms of improvement in throughput,more so than response time. however, quantifying the actual benefit canbe done only by actually rewriting the program to reduce footprint,running the program, and comparing the throughput with previous runs.Thus, programmers use a ‘trial and error’ approach in optimizing garbagecollection. A utility called verbosegc in Java allows programmers togather garbage collection statistics such as the number of garbagecollection events, duration of the garbage collection, etc. However,there is no systematic way to determine change in performanceimmediately without modifying the program and obtaining measurementsuntil a desired result is reached. This trial and error approach can betime consuming, as a program may need to be modified several times andtested each time to see if the performance target has been reached.

Therefore, it would be advantageous to have an improved method andapparatus for determining a close approximate benefit of reducingfootprint of a Java application in a systematic manner without using aniterative ‘trial and error’ approach. This is very useful in situationswhere there is a target performance throughput and so by knowing the gapbetween the current throughput and the target throughput, getting anidea of how much reduction in memory footprint is needed to close thegap will facilitate the whole process.

SUMMARY OF THE INVENTION

The present invention determines the effect on a Java program caused bymodification of the program. The present invention provides a method,apparatus, and computer instructions for improving performance in a Javaprogram by checking to see if assumptions about the system are satisfiedand by collecting information about garbage collection. Using thisinformation and the assumptions formed, a mathematical model forrepresenting the effect of modifications to the program on garbagecollection events is made. This model is used to estimate the effect onthe program caused by modifications.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features believed characteristic of the invention are setforth in the appended claims. The invention itself, however, as well asa preferred mode of use, further objectives and advantages thereof, willbest be understood by reference to the following detailed description ofan illustrative embodiment when read in conjunction with theaccompanying drawings, wherein:

FIG. 1 shows a computer system consistent with implementing a preferredembodiment of the present invention.

FIG. 2 shows a block diagram of a computer system consistent withimplementing a preferred embodiment of the present invention.

FIG. 3 shows a diagram of a relational components of a Java-based systemconsistent with implementing a preferred embodiment of the presentinvention.

FIG. 4 shows a Java Virtual Machine (JVM) consistent with implementing apreferred embodiment of the present invention.

FIG. 5 shows an overview process flow consistent with implementing apreferred embodiment of the present invention.

FIG. 6 shows a detailed process flow consistent with implementing apreferred embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

With reference now to the figures and in particular with reference toFIG. 1, a pictorial representation of a data processing system in whichthe present invention may be implemented is depicted in accordance witha preferred embodiment of the present invention. A computer 100 isdepicted which includes a system unit 102, a video display terminal 104,a keyboard 106, storage devices 108, which may include floppy drives andother types of permanent and removable storage media, and mouse 110.Additional input devices may be included with personal computer 100,such as, for example, a joystick, touchpad, touch screen, trackball,microphone, and the like. Computer 100 can be implemented using anysuitable computer, such as an IBM RS/6000 computer or IntelliStationcomputer, which are products of International Business MachinesCorporation, located in Armonk, N.Y. Although the depictedrepresentation shows a computer, other embodiments of the presentinvention may be implemented in other types of data processing systems,such as a network computer. Computer 100 also preferably includes agraphical user interface that may be implemented by means of systemssoftware residing in computer readable media in operation withincomputer 100.

With reference now to FIG. 2, a block diagram of a data processingsystem is shown in which the present invention may be implemented. Dataprocessing system 200 is an example of a computer, such as computer 100in FIG. 1, in which code or instructions implementing the processes ofthe present invention may be located. Data processing system 200 employsa peripheral component interconnect (PCI) local bus architecture.Although the depicted example employs a PCI bus, other bus architecturessuch as Accelerated Graphics Port (AGP) and Industry StandardArchitecture (ISA) may be used. Processor 202 and main memory 204 areconnected to PCI local bus 206 through PCI bridge 208. PCI bridge 208also may include an integrated memory controller and cache memory forprocessor 202. Additional connections to PCI local bus 206 may be madethrough direct component interconnection or through add-in boards. Inthe depicted example, local area network (LAN) adapter 210, smallcomputer system interface SCSI host bus adapter 212, and expansion businterface 214 are connected to PCI local bus 206 by direct componentconnection. In contrast, audio adapter 216, graphics adapter 218, andaudio/video adapter 219 are connected to PCI local bus 206 by add-inboards inserted into expansion slots. Expansion bus interface 214provides a connection for a keyboard and mouse adapter 220, modem 222,and additional memory 224. SCSI host bus adapter 212 provides aconnection for hard disk drive 226, tape drive 228, and CD-ROM drive230. Typical PCI local bus implementations will support three or fourPCI expansion slots or add-in connectors.

An operating system runs on processor 202 and is used to coordinate andprovide control of various components within data processing system 200in FIG. 2. The operating system may be a commercially availableoperating system such as Windows 2000, which is available from MicrosoftCorporation. An object oriented programming system such as Java may runin conjunction with the operating system and provides calls to theoperating system from Java programs or applications executing on dataprocessing system 200. “Java” is a trademark of Sun Microsystems, Inc.Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as hard disk drive 226, and may be loaded into main memory 204 forexecution by processor 202.

Those of ordinary skill in the art will appreciate that the hardware inFIG. 2 may vary depending on the implementation. Other internal hardwareor peripheral devices, such as flash ROM (or equivalent nonvolatilememory) or optical disk drives and the like, may be used in addition toor in place of the hardware depicted in FIG. 2. Also, the processes ofthe present invention may be applied to a multiprocessor data processingsystem.

For example, data processing system 200, if optionally configured as anetwork computer, may not include SCSI host bus adapter 212, hard diskdrive 226, tape drive 228, and CD-ROM 230, as noted by dotted line 232in FIG. 2 denoting optional inclusion. In that case, the computer, to beproperly called a client computer, must include some type of networkcommunication interface, such as LAN adapter 210, modem 222, or thelike. As another example, data processing system 200 may be astand-alone system configured to be bootable without relying on sometype of network communication interface, whether or not data processingsystem 200 comprises some type of network communication interface. As afurther example, data processing system 200 may be a personal digitalassistant (PDA), which is configured with ROM and/or flash ROM toprovide non-volatile memory for storing operating system files and/oruser-generated data.

The depicted example in FIG. 2 and above-described examples are notmeant to imply architectural limitations. For example, data processingsystem 200 also may be a notebook computer or hand held computer inaddition to taking the form of a PDA. Data processing system 200 alsomay be a kiosk or a Web appliance.

The processes of the present invention are performed by processor 202using computer implemented instructions, which may be located in amemory such as, for example, main memory 204, memory 224, or in one ormore peripheral devices 226-230.

With reference now to FIG. 3, a block diagram illustrates therelationship of software components operating within a computer systemthat may implement the present invention. Java-based system 300 containsplatform specific operating system 302 that provides hardware and systemsupport to software executing on a specific hardware platform. JVM 304is one software application that may execute in conjunction with theoperating system. JVM 304 provides a Java run-time environment with theability to execute Java application or applet 306, which is a program,servlet, or software component written in the Java programming language.The computer system in which JVM 304 operates may be similar to dataprocessing system 200 or computer 100 described above. However, JVM 304may be implemented in dedicated hardware on a so-called Java chip,Java-on-silicon, or Java processor with an embedded picoJava core.

At the center of a Java run-time environment is the JVM, which supportsall aspects of Java's environment, including its architecture, securityfeatures, mobility across networks, and platform independence.

The JVM is a virtual computer, i.e. a computer that is specifiedabstractly. The specification defines certain features that every JVMmust implement, with some range of design choices that may depend uponthe platform on which the JVM is designed to execute. For example, allJVMs must execute Java bytecodes and may use a range of techniques toexecute the instructions represented by the bytecodes. A JVM may beimplemented completely in software or somewhat in hardware. Thisflexibility allows different JVMs to be designed for mainframe computersand PDAs.

The JVM is the name of a virtual computer component that actuallyexecutes Java programs. Java programs are not run directly by thecentral processor but instead by the JVM, which is itself a piece ofsoftware running on the processor. The JVM allows Java programs to beexecuted on a different platform as opposed to only the one platform forwhich the code was compiled. Java programs are compiled for the JVM. Inthis manner, Java is able to support applications for many types of dataprocessing systems, which may contain a variety of central processingunits and operating systems architectures. To enable a Java applicationto execute on different types of data processing systems, a compilertypically generates an architecture-neutral file format—the compiledcode is executable on many processors, given the presence of the Javarun-time system. The Java compiler generates bytecode instructions thatare nonspecific to a particular computer architecture. A bytecode is amachine independent code generated by the Java compiler and executed bya Java interpreter. A Java interpreter is part of the JVM thatalternately decodes and interprets a bytecode or bytecodes. Thesebytecode instructions are designed to be easy to interpret on anycomputer and easily translated on the fly into native machine code. Bytecode may be translated into native code by a just-in-time compiler orJIT.

A JVM loads class files and executes the bytecodes within them. Theclass files are loaded by a class loader in the JVM. The class loaderloads class files from an application and the class files from the Javaapplication programming interfaces (APIs) which are needed by theapplication. The execution engine that executes the bytecodes may varyacross platforms and implementations.

One type of software-based execution engine is a just-in-time compiler.With this type of execution, the bytecodes of a method are compiled tonative machine code upon successful fulfillment of some type of criteriafor jitting a method. The native machine code for the method is thencached and reused upon the next invocation of the method. The executionengine may also be implemented in hardware and embedded on a chip sothat the Java bytecodes are executed natively. JVMs usually interpretbytecodes, but JVMs may also use other techniques, such as just-in-timecompiling, to execute bytecodes.

When an application is executed on a JVM that is implemented in softwareon a platform-specific operating system, a Java application may interactwith the host operating system by invoking native methods. A Java methodis written in the Java language, compiled to bytecodes, and stored inclass files. A native method is written in some other language andcompiled to the native machine code of a particular processor. Nativemethods are stored in a dynamically linked library whose exact form isplatform specific.

With reference now to FIG. 4, a block diagram of a JVM is depicted inaccordance with a preferred embodiment of the present invention. JVM 400includes a class loader subsystem 402, which is a mechanism for loadingtypes, such as classes and interfaces, given fully qualified names. JVM400 also contains runtime data areas 404, execution engine 406, nativemethod interface 408, and memory management 410. Execution engine 406 isa mechanism for executing instructions contained in the methods ofclasses loaded by class loader subsystem 402. Execution engine 406 maybe, for example, Java interpreter 412 or just-in-time compiler 414.Native method interface 408 allows access to resources in the underlyingoperating system. Native method interface 408 may be, for example, aJava native interface.

Runtime data areas 404 contain native method stacks 416, Java stacks418, PC registers 420, method area 422, and heap 424. These differentdata areas represent the organization of memory needed by JVM 400 toexecute a program.

Java stacks 418 are used to store the state of Java method invocations.When a new thread is launched, the JVM creates a new Java stack for thethread. The JVM performs only two operations directly on Java stacks: itpushes and pops frames. A thread's Java stack stores the state of Javamethod invocations for the thread. The state of a Java method invocationincludes its local variables, the parameters with which it was invoked,its return value, if any, and intermediate calculations. Java stacks arecomposed of stack frames. A stack frame contains the state of a singleJava method invocation. When a thread invokes a method, the JVM pushes anew frame onto the Java stack of the thread. When the method completes,the JVM pops the frame for that method and discards it. The JVM does nothave any registers for holding intermediate values; any Java instructionthat requires or produces an intermediate value uses the stack forholding the intermediate values. In this manner, the Java instructionset is well-defined for a variety of platform architectures.

PC registers 420 are used to indicate the next instruction to beexecuted. Each instantiated thread gets its own pc register (programcounter) and Java stack. If the thread is executing a JVM method, thevalue of the pc register indicates the next instruction to execute. Ifthe thread is executing a native method, then the contents of the pcregister are undefined. Native method stacks 414 store the state ofinvocations of native methods. The state of native method invocations isstored in an implementation-dependent way in native method stacks,registers, or other implementation-dependent memory areas. In some JVMimplementations, native method stacks 414 and Java stacks 416 arecombined.

Method area 422 contains class data while heap 424 contains allinstantiated objects. The JVM specification strictly defines data typesand operations. Most JVMs choose to have one method area and one heap,each of which are shared by all threads running inside the JVM. When theJVM loads a class file, it parses information about a type from thebinary data contained in the class file. It places this type informationinto the method area. Each time a class instance or array is created,the memory for the new object is allocated from heap 424. JVM 400includes an instruction that allocates memory space within the memoryfor heap 424 but includes no instruction for freeing that space withinthe memory. Memory management 410 in the depicted example manages memoryspace within the memory allocated to heap 424. Memory management 410 mayinclude a garbage collector, which automatically reclaims memory used byobjects that are no longer referenced. Additionally, a garbage collectoralso may move objects to reduce heap fragmentation.

In a preferred embodiment, the present invention provides a system andmethod for taking information from an application about garbagecollection and deducing changes in performance that will result frommodifying the program. Information is preferably obtained about garbagecollection through verbosegc, which provides information about garbagecollection events in a text file.

The present invention describes a mathematical system for determiningthe likely effects on program performance resulting from programmodification. The mathematical aspects of the present invention can bedone manually or programmed into a computer system for automaticexecution.

Performance benefit in a program may be gained by reducing actualgarbage collection and object creation. These two tasks have associatedcosts, and by reducing their frequency and/or duration of execution, theassociated cost to the system (in delayed or suspended threads, forexample) is also reduced. The direct cost of garbage collection is thepausing time which affects response time as well as throughput. In apreferred embodiment, the present invention uses two variables whencalculating the cost of garbage collection: the duration of the garbagecollection, and the frequency of the garbage collection.

By minimizing either or both of these variables, the cost of garbagecollection is minimized. The duration of garbage collection (i.e., thepausing time imposed on other threads during garbage collection events)depends largely on several variables. For example, the amount of garbagethat must be cleaned up, the algorithm used to do the collecting orcopying, the heap compaction, reconciling object references that aremoved, and the number and nature of finalizers that must be executed.

The frequency of garbage collection is influenced by the rate of objectcreation, the heap fragmentation, the size of the heap, and the garbagecollection policy constraints that may exist in a system. Overall, theduration and frequency of garbage collection events is difficult topredict because they depend heavily on the garbage collection algorithmused, the lifetime of the objects, the allocation rate, and the size ofthe heap.

The present invention uses a mathematical method to derive a function topredict performance effect caused by program modification. For example,a preferred embodiment of the function estimates how much time will besaved on garbage collection by reducing memory by a given amount.

In this example embodiment, a multivariate function is derivedy=F(m,t,g,d,f)where m is the amount of memory to be removed from live memory (a.k.a.footprint), t is the current computed throughput (measured intransactions per second), d is the total duration of the programexecution from which t was computed, g is the total time spent ongarbage collection within the duration d (thus g<d), f is the averagefootprint during duration d. This expression is used to find y which isthe number of transactions gained after reducing the footprint to f−mover the same duration d.

Given the amount of memory to be deducted from the current footprint, itis desired to know how many additional transactions can be computedgiven the current computed transaction, the total duration of the run,and the portion of that run spent in garbage collection.

In a preferred embodiment, the model imposes certain assumptions. Oneexample set of assumptions follows: After a warm-up run, the systemsettles into a steady state where the footprint levels off as well asgarbage and maximum heap size; the throughput t is computed within aduration d during which the steady state has been observed; a verbosegcoutput during the steady state has been taken; the exact nature of thegarbage collection algorithm is immaterial; the duration d is fixed at aconstant value and is not affected by changes in other parameters; andthe garbage generation rate is constant.

The following information is obtainable from the verbosegc output, andin a preferred embodiment, variables are assigned as follows:

Total number of GC: c

Total time spent in GC: g

Current heap size: HeapSize

Total duration of the run: d

Average GC time: GCavg

Average GC interval: GCINTavg

Average garbage collected per GC: GARBAGEavg

Average live memory per GC: LIVEMEMavg.

A steady state is defined as occurring when:

the standard deviation from GCINTavg<5%

the standard deviation from GCavg<5%

the standard deviation from GARBAGEavg<5%

the standard deviation from LIVEMEMavg<5%

the HeapSize is the same all throughout.

Given these variables and assumptions, an example embodiment of themethod for estimating performance based on the modification isdiscussed. The basic idea is to transfer time spent on garbagecollection into time actually spent used to do actual work.Consequently, pausing time will be reduced and additional throughput canbe computed given that the duration of the run remains constant. Fromthe given information, it is possible to compute the percentage of timethat was spent for garbage collection and for doing actual work. It isknown that the most expensive works in garbage collection are markingand compaction, both of which are proportional to the amount of livememory. Then we can very roughly assume that all of the garbagecollection time is spent on the live memory. With the system in a steadystate, the average amount of live memory and the average length ofgarbage collection may be used to represent a typical garbage collectionevent. Therefore the amount of time spent on each memory unit (in bytes)can be computed by dividing GCavg by LIVEMEMavg.

Let q=GCavg/LIVEMEMavg. The result has units of sec/bytes. Thus,reducing the amount of memory by m results in m*q (bytes*sec/bytes)seconds saved in doing garbage collection (where “*” representsmultiplication). This time can be spent doing work. The GC count or calso decreases as there will be less occurrences of GC events by virtueof the assumptions that (a) object allocation rate is the same, and (b)total heap is the same. We can estimate what c′ (c after reducing thefootprint by m) will be under this situation. The GC count is inverselyproportional to HeapSize−(f−m), that is, the bigger the available spacefor garbage, the less garbage collection will occur. Thus,c′=[(HeapSize−f)/(HeapSize−(f−m))]*cHere it is known that c′ is always less than c because[(HeapSize−f)/(HeapSize−(f−m))] is always less than one. Note that f>msince you cannot reduce more than what the current footprint is. Now,the new total garbage collection time g′ can be computed, which isdetermined by g′=g−c′*(m*q). Recall that M*q is the amount of time savedfrom doing a garbage collection. Thus, c′*(m*q) is the total amount oftime saved not doing GC. Meanwhile, d−g is the total amount of timespent doing actual work. Consequently, d−g′ is the total amount of timespent doing actual work after reducing the footprint f by m. Usingratios and proportions,t/(d−g) as to t′/(d−g′)yieldst′=(d−g′)/(d−g)*twhich yields a result in which t′>t because (d−g′)/(d−g) is alwaysgreater than or equal to one. Thus,y=t′−tand the computed improvement factor is determined to be(d−g′)/(d−g)−1

These calculations are only one example of how the present invention canbe implemented. Other assumptions can be made, and other variables canbe used or omitted from the above described model without deviating fromthe spirit of the present invention. The primary concern of the presentinvention is the ability to predict or estimate the improvement in termsof throughput in a Java program based on the amount of memory footprintused by the program. If this can be done then we can easily provide atool that simulates improvements instead of measuring them empirically.Once the right amount of memory reduction has been determined to achievethe desired throughput, modification of the program can be made toachieve the memory footprint reduction.

FIG. 5 shows a process flow for implementing a preferred embodiment ofthe present invention. Though the described calculations can beperformed manually, they can also be automated and programmed to beperformed by a computer system, such as system 100 of FIG. 1. In thisgeneralization of the innovative process, information from anapplication relating to garbage collection is obtained (step 502). Next,the system is tested to see if it satisfies the “steady state”assumptions, as described above (step 504), such as, for example, thenature of a steady state of the system, the throughput of the system,the nature of the garbage collection algorithm of the system, theduration of garbage collection events, and the garbage generation rate.Note that these are only examples and are not intended to limit thescope of the present invention. Finally, given the obtained informationand assumptions, an estimate is formed about how much time will be savedon garbage collection by reducing memory of the program footprint by agiven amount (step 506), with the process terminating thereafter.

FIG. 6 shows a more detailed example of implementing a preferredembodiment of the present invention. Again, such an analysis as ispresented can be performed manually or by a computer program designed toexecute the innovative method, such as computer system 100 of FIG. 1.First, information is obtained about garbage collection (step 602).Next, assumptions are formed about the computer system and program (step604). An average duration of garbage collection events and a frequencyof garbage collection events is determined (step 606). Next, apercentage of time spent on garbage collection is estimated (step 608)and a percentage of time spent doing other work in the computer systemis estimated (step 610). Next, a typical garbage collection event isestimated using the average amount of live memory and length of garbagecollection events (step 612). The amount of time spent on each memoryunit for garbage collection is calculated (step 614). Finally, an amountof time saved doing garbage collection by reducing memory by a givenamount is calculated (step 616). Finally, the quantifiable performanceimprovements of the system, i.e., the additional throughput garneredfrom the time savings, is calculated (step 618), with the processterminating thereafter. This can be represented ast′=(d−g′)/(d−g)*twhere t′ is the new throughput while t was the previous throughputbefore reducing the footprint. Thus, y′ (which is the throughputimprovement) is simply t′−t.

It is important to note that while the present invention has beendescribed in the context of a fully functioning data processing system,those of ordinary skill in the art will appreciate that the processes ofthe present invention are capable of being distributed in the form of acomputer readable medium of instructions and a variety of forms and thatthe present invention applies equally regardless of the particular typeof signal bearing media actually used to carry out the distribution.Examples of computer readable media include recordable-type media, suchas a floppy disk, a hard disk drive, a RAM, CD-ROMs, DVD-ROMs, andtransmission-type media, such as digital and analog communicationslinks, wired or wireless communications links using transmission forms,such as, for example, radio frequency and light wave transmissions. Thecomputer readable media may take the form of coded formats that aredecoded for actual use in a particular data processing system.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the art. Theembodiment was chosen and described in order to best explain theprinciples of the invention, the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

1. A method, within a computer hardware system having a heap, ofimproving performance in a Java-based computer program, comprising:executing the computer program within a virtual machine; identifyingwhen the computer program enters a steady state; only after the computerprogram being within the steady state; obtaining information regardinggarbage collection associated with the computer program; and deducingchanges in performance of the computer program that will result frommodifying the computer program, wherein the steady state is defined aswhen a standard deviation from an average garbage collection interval isless than 5%.
 2. The method of claim 1, further comprising estimating acost of the garbage collection to the performance by a duration of anaverage garbage collection event, and a frequency of garbage collectionevents.
 3. The method of claim 2, wherein the duration depends upon anamount of garbage to be cleaned up, an algorithm used to perform thegarbage collection; a heap compaction, a cost of reconciling objectreferences that are moved, and a number of finalizers to be executed. 4.The method of claim 2, wherein the frequency depends upon a rate ofobject creation, a heap fragmentation, a size of a heap, and a garbagecollection policy.
 5. The method of claim 1, further comprising changingthe computer program by reducing an amount of memory from a footprint ofthe computer program.
 6. The method of claim 5, further comprisingdetermining a number of additional transactions that can be executedduring a run based upon the amount of the memory reduced from thefootprint, a total duration for the run, and an amount of time the runis spent in garbage collection.
 7. The method of claim 1, wherein theinformation associated with the garbage collection is obtained from averbosegc.
 8. A computer hardware system configured to improve aperformance in a Java-based computer program, comprising: a memoryincluding a heap; and a processor, wherein the processor is configuredto perform: executing the computer program within a virtual machine;identifying when the computer program enters a steady state; only afterthe computer program being within the steady state; obtaininginformation regarding garbage collection associated with the computerprogram; and deducing changes in performance of the computer programthat will result from modifying the computer program, wherein the steadystate is defined as when a standard deviation from an average garbagecollection time is less than 5%.
 9. The computer hardware system ofclaim 8, wherein a cost of the garbage collection to the performance isestimated using a duration of an average garbage collection event, and afrequency of garbage collection events.
 10. The computer hardware systemof claim 9, wherein the duration depends upon an amount of garbage to becleaned up, an algorithm used to perform the garbage collection; a heapcompaction, a cost of reconciling object references that are moved, anda number of finalizers to be executed.
 11. The computer hardware systemof claim 9, wherein the frequency depends upon a rate of objectcreation, a heap fragmentation, a size of a heap, and a garbagecollection policy.
 12. The computer hardware system of claim 8, whereinthe processor is further configured to perform: changing the computerprogram by reducing an amount of memory from a footprint of the computerprogram.
 13. The computer hardware system of claim 12, wherein theprocessor is further configured to perform: determining a number ofadditional transactions that can be executed during a run based upon theamount of the memory reduced from the footprint, a total duration forthe run, and an amount of time the run is spent in garbage collection.14. A computer program product comprising a computer usable storagemedium having stored therein computer usable program code for improvingperformance in a Java-based computer program, the computer usableprogram code, which when executed by a computer hardware system, causesthe computer hardware system to perform: executing the computer programwithin a virtual machine; identifying when the computer program enters asteady state; only after the computer program being within the steadystate; obtaining information regarding garbage collection associatedwith the computer program; and deducing changes in performance of thecomputer program that will result from modifying the computer program,wherein the steady state is defined as when a standard deviation from anaverage garbage collected per garbage collection is less than 5%. 15.The computer program product of claim 14, wherein the computer usableprogram code further causes the computer hardware system to performestimating a cost of the garbage collection to the performance by aduration of an average garbage collection event, and a frequency ofgarbage collection events.
 16. The computer program product of claim 15,wherein the duration depends upon an amount of garbage to be cleaned up,an algorithm used to perform the garbage collection; a heap compaction,a cost of reconciling object references that are moved, and a number offinalizers to be executed.
 17. The computer program product of claim 15,wherein the frequency depends upon a rate of object creation, a heapfragmentation, a size of a heap, and a garbage collection policy. 18.The computer program product of claim 14, wherein the computer usableprogram code further causes the computer hardware system to performchanging the computer program by reducing an amount of memory from afootprint of the computer program.
 19. The computer program product ofclaim 18, wherein the computer usable program code further causes thecomputer hardware system to perform determining a number of additionaltransactions that can be executed during a run based upon the amount ofthe memory reduced from the footprint, a total duration for the run, andan amount of time the run is spent in garbage collection.
 20. Thecomputer program product of claim 14, wherein the information associatedwith the garbage collection is obtained from a verbosegc.
 21. A method,within a computer hardware system having a heap, of improvingperformance in a Java-based computer program, comprising: executing thecomputer program within a virtual machine; identifying when the computerprogram enters a steady state; only after the computer program beingwithin the steady state; obtaining information regarding garbagecollection associated with the computer program; and deducing changes inperformance of the computer program that will result from modifying thecomputer program, wherein the steady state is defined as when a standarddeviation from an average live memory per garbage collection is lessthan 5%.